The emergence of novelties and their rise and fall in popularity is an ubiquitous phenomenon in human activities. The coexistence of popular evergreens with novel and sometimes ephemeral trends pervades technological, scientific and artistic production. Though this phenomenon is very intuitively captured by our common sense, a comprehensive explanation of how waves of novelties are not hampered by well established old-comers is still lacking. Here we first quantify this phenomenology by empirically looking at different systems that display innovation at very different levels: the creation of hashtags in Twitter, the evolution of online code repositories, the creation of texts and the listening of songs on online platforms. In all these systems surprisingly similar patterns emerge as the non-trivial outcome of two contrasting forces: the tendency of retracing already explored avenues (exploit) and the inclination to explore new possibilities. These findings are naturally explained in the framework of the expansion of the adjacent possible, a recently introduced theoretical framework that postulates the restructuring of the space of possibilities conditional to the occurrence of innovations. The predictions of our theoretical framework are borne out in all the phenomenologies investigated, paving the way to a better understanding and control of innovation processes.

In the last decades, the acceleration of urban growth has led to an unprecedented level of urban interactions and interdependence. This situation calls for a significant effort among the scientific community to come up with engaging and meaningful visualizations and accessible scenario simulation engines. The present paper gives a contribution in this direction by providing general methods to evaluate accessibility in cities based on public transportation data. Through the notion of isochrones, the accessibility quantities proposed measure the performance of transport systems at connecting places and people in urban systems. Then we introduce scores ranking cities according to their overall accessibility. We highlight significant inequalities in the distribution of these measures across the population, which are found to be strikingly similar across various urban environments. Our results are released through the interactive platform: www.citychrone.org , aimed at providing the community at large with a useful tool for awareness and decision-making.

Air Transportation represents a very interesting example of a complex techno-social system whose importance has considerably grown in time and whose management requires a careful understanding of the subtle interplay between technological infrastructure and human behavior. Despite the competition with other transportation systems, a growth of air traffic is still foreseen in Europe for the next years. The increase of traffic load could bring the current Air Traffic Network above its capacity limits so that safety standards and performances might not be guaranteed anymore. Lacking the possibility of a direct investigation of this scenario, we resort to computer simulations in order to quantify the disruptive potential of an increase in traffic load. To this end we model the Air Transportation system as a complex dynamical network of flights controlled by humans who have to solve potentially dangerous conflicts by redirecting aircraft trajectories. The model is driven and validated through historical data of flight schedules in a European national airspace. While correctly reproducing actual statistics of the Air Transportation system, e.g., the distribution of delays, the model allows for theoretical predictions. Upon an increase of the traffic load injected in the system, the model predicts a transition from a phase in which all conflicts can be successfully resolved, to a phase in which many conflicts cannot be resolved anymore. We highlight how the current flight density of the Air Transportation system is well below the transition, provided that controllers make use of a special re-routing procedure. While the congestion transition displays a universal scaling behavior, its threshold depends on the conflict solving strategy adopted. Finally, the generality of the modeling scheme introduced makes it a flexible general tool to simulate and control Air Transportation systems in realistic and synthetic scenarios.

One of the main challenges in comparative studies on populism concerns its temporal and spatial measurements within and between a large number of parties and countries. Textual analysis has proved useful for these purposes, and automated methods can further improve research in this direction. Here, we propose a method to derive a score of parties’ levels of populism using supervised machine learning to perform textual analysis on national manifestos. We illustrate the advantages of our approach, which allows for measuring populism for a vast number of parties and countries without resource-intensive human-coding processes and provides accurate, updated information for temporal and spatial comparisons of populism. Furthermore, our method allows for obtaining a continuous score of populism, which ensures more fine-grained analyses of the party landscape while reducing the risk of arbitrary classifications. To illustrate the potential contribution of this score, we use it as a proxy for parties’ levels of populism, analyzing average trends in six European countries from the early 2000s for nearly two decades.

Railways are a key infrastructure for any modern country. The reliability and resilience of this peculiar transportation system may be challenged by different shocks such as disruptions, strikes and adverse weather conditions. These events compromise the correct functioning of the system and trigger the spreading of delays into the railway network on a daily basis. Despite their importance, a general theoretical understanding of the underlying causes of these disruptions is still lacking. In this work, we analyse the Italian and German railway networks by leveraging on the train schedules and actual delay data retrieved during the year 2015. We use these data to infer simple statistical laws ruling the emergence of localized delays in different areas of the network and we model the spreading of these delays throughout the network by exploiting a framework inspired by epidemic spreading models. Our model offers a fast and easy tool for the preliminary assessment of the effectiveness of traffic handling policies, and of the railway network criticalities.